---
title: "Reference: Textual Difference Scorer | Evals"
description: Documentation for the Textual Difference Scorer in Mastra, which measures textual differences between strings using sequence matching.
---

# Textual Difference Scorer

The `createTextualDifferenceScorer()` function uses sequence matching to measure the textual differences between two strings. It provides detailed information about changes, including the number of operations needed to transform one text into another.

## Parameters

The `createTextualDifferenceScorer()` function does not take any options.

This function returns an instance of the MastraScorer class. See the [MastraScorer reference](./mastra-scorer) for details on the `.run()` method and its input/output.

## .run() Returns

<PropertiesTable
  content={[
    {
      name: "runId",
      type: "string",
      description: "The id of the run (optional).",
    },
    {
      name: "analyzeStepResult",
      type: "object",
      description:
        "Object with difference metrics: { confidence: number, changes: number, lengthDiff: number }",
    },
    {
      name: "score",
      type: "number",
      description: "Similarity ratio (0-1) where 1 indicates identical texts.",
    },
  ]}
/>

`.run()` returns a result in the following shape:

```typescript
{
  runId: string,
  analyzeStepResult: {
    confidence: number,
    ratio: number,
    changes: number,
    lengthDiff: number
  },
  score: number
}
```

## Scoring Details

The scorer calculates several measures:

- **Similarity Ratio**: Based on sequence matching between texts (0-1)
- **Changes**: Count of non-matching operations needed
- **Length Difference**: Normalized difference in text lengths
- **Confidence**: Inversely proportional to length difference

### Scoring Process

1. Analyzes textual differences:
   - Performs sequence matching between input and output
   - Counts the number of change operations required
   - Measures length differences
2. Calculates metrics:
   - Computes similarity ratio
   - Determines confidence score
   - Combines into weighted score

Final score: `(similarity_ratio * confidence) * scale`

### Score interpretation

A textual difference score between 0 and 1:

- **1.0**: Identical texts – no differences detected.
- **0.7–0.9**: Minor differences – few changes needed.
- **0.4–0.6**: Moderate differences – noticeable changes required.
- **0.1–0.3**: Major differences – extensive changes needed.
- **0.0**: Completely different texts.

## Example

Measure textual differences between expected and actual agent outputs:

```typescript title="src/example-textual-difference.ts" showLineNumbers copy
import { runEvals } from "@mastra/core/evals";
import { createTextualDifferenceScorer } from "@mastra/evals/scorers/prebuilt";
import { myAgent } from "./agent";

const scorer = createTextualDifferenceScorer();

const result = await runEvals({
  data: [
    {
      input: "Summarize the concept of recursion",
      groundTruth:
        "Recursion is when a function calls itself to solve a problem by breaking it into smaller subproblems.",
    },
    {
      input: "What is the capital of France?",
      groundTruth: "The capital of France is Paris.",
    },
  ],
  scorers: [scorer],
  target: myAgent,
  onItemComplete: ({ scorerResults }) => {
    console.log({
      score: scorerResults[scorer.id].score,
      groundTruth: scorerResults[scorer.id].groundTruth,
    });
  },
});

console.log(result.scores);
```

For more details on `runEvals`, see the [runEvals reference](/reference/v1/evals/run-evals).

To add this scorer to an agent, see the [Scorers overview](/docs/v1/evals/overview#adding-scorers-to-agents) guide.

## Related

- [Content Similarity Scorer](./content-similarity)
- [Completeness Scorer](./completeness)
- [Keyword Coverage Scorer](./keyword-coverage)
